Systems and methods for using statistical inference to enhance the precision of sparsified capacitive-touch and other human-interface devices

ABSTRACT

In one embodiment, a method includes by an electronic device: receiving sensor data indicative of a touch input from sensors of a human interface-device (HID) of the electronic device, where the touch input occurs at a set of actual coordinates with respect to the HID, and where the sensor data indicates the touch input occurs at a set of detected coordinates with respect to the HID, determining a context associated with the touch input, determining, by one or more generative models, context-dependent statistics to apply a delta change to the set of detected coordinates, where the context-dependent statistics are based on the context associated with the touch input, and where the one or more generative models comprises one or more system parameters and one or more latent parameters, and determining a set of time-lapsed predicted coordinates of the touch input with respect to the HID based on the delta change.

TECHNICAL FIELD

This disclosure relates generally to display technology, and inparticular sensors in a human-interface device.

BACKGROUND

Current touch input technologies become expensive when scaled to largesurfaces or non-flat applications, such as large-scale non-flat (e.g.,curved) TVs. Most of the touchscreen technologies are manufactured ontoa rigid glass substrate using a multi-layered, row-column matrix usinghigh conductive material such as ITO. Therefore, the cost for largetouch screens is high. In addition, there is a lack of flexible andnon-flat (or irregular shaped) touch capability surfaces.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example machine-learning system.

FIG. 2 illustrates an example display with a sparsified sensorstructure.

FIG. 3 illustrates an example circuit of a sparsified sensor structure.

FIG. 4 illustrates an example power spectral density chart of asparsified sensor structure.

FIG. 5A illustrates an example touch coordinate prediction from touchinputs.

FIG. 5B illustrates an example time series of touch inputs plotted alonga time index.

FIG. 6 illustrates an example human-interface device.

FIG. 7 illustrates an example process flow of a human-interface device.

FIGS. 8A-8B illustrate an example process of estimating a set of actualcoordinates associated with a touch input.

FIGS. 9A-9B illustrate an example post-processing applied to touchinputs.

FIG. 10 illustrates an example training process of estimating a set ofactual coordinates associated with a touch input.

FIG. 11 illustrates an example method for determining a set of predictedcoordinates of a touch input.

FIG. 12 illustrates an example computer system.

FIG. 13 illustrates a diagram of an example artificial intelligence (AI)architecture.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Machine-Learning System Overview

FIG. 1 illustrates an example prediction system 100. As depicted by FIG.1 , the prediction system 100 may include hardware 106, a preprocessor108, a buffer 110, a prediction block 112, and a statistical time-lapsemodel 114. In particular embodiments, one or more components of theprediction system 100 can include a computing engine. As an example andnot by way of limitation, the statistical time-lapse model 114 caninclude a computing engine. The prediction system 100 may be used toaccurately predict actual coordinates of a touch input. Additionally,the prediction system 100 may be utilized to process and manage variousanalytics and/or data intelligence such as predicting the actualcoordinates of a touch input, and so forth. In particular embodiments,the prediction system 100 can include a cloud-based cluster computingarchitecture, client-based computing architecture, or other similarcomputing architecture that may receive a user action 102 where acontext 104 is associated with the user action. In particularembodiments, the user action 102 can include one or more of a touchaction, gesture action, haptic action, and the like. In particularembodiments, the context 104 of the user action can include one or moreof an environment, type of activity, and the like. In particularembodiments, the context 104 may not be explicitly measured, but mayimpact the measurement of the user action 102. As an example and not byway of limitation, the user action 102 may correspond to a set of touchcoordinates (e.g., a detected X-coordinate and a detected Y-coordinate).The context 104 may affect the set of touch coordinates (e.g., apply adelta to one or both of the detected X-coordinate and the detectedY-coordinate).

In particular embodiments, the context 104 may include informationcorresponding to one or more system parameters and one or more latentparameters. The system parameters may include one or more of raw orprocessed sensor data. Latent parameters may include one or more knownlatent parameters and one or more unknown latent parameters. As anexample and not by way of limitation, the latent parameters may includeone or more of environmental conditions, device locations, event times,etc. Each of these example latent parameters may either be known latentparameters or unknown latent parameters.

In particular embodiments, the hardware 106 can comprise one or moreinput devices (e.g., tactile and haptic human-interface devices (HID)technologies, such as touch sensing technologies, acoustic touch panels,3D hand localization, and the like) as described herein. The hardware106 can be configured to receive a user action 102 that has a context104 and output raw electrical signals corresponding to the action {tildeover (d)}. In particular embodiments, the preprocessor 108 may receivethe raw electric signals from the hardware 106. In particularembodiments, the preprocessor 108 can process the raw electric signalsfrom the hardware 106 to generate a set of detected coordinates withrespect to the HID. For example, the user action 102 may correspond to aset of X-coordinates and a set of corresponding Y-coordinates withrespect to the HID. The set of detected coordinates with respect to theHID can correspond to coordinates that are registered by the HID. Forexample, as a result of one or more sensor placements within the HID,the detected coordinate corresponding to a touch input may not be thesame as an actual coordinate corresponding to the user action 102 (e.g.,a touch input). This may be the result of one or more factors related toHID design, regime of operation, or its operating environment expressedas coherent and incoherent noise.

In particular embodiments, the hardware 106 can send the raw electricalsignals, {tilde over (d)} to the buffer 110. In particular embodiments,the buffer 110 can receive the preprocessed raw signals, d from thepreprocessor 108. In particular embodiments, if the buffer 110 receivesraw electrical signals, {tilde over (d)} directly from the hardware 106,the buffer may equate {tilde over (d)}=d. In particular embodiments, thebuffer 110, may place the raw and/or pre-processed signals in a buffer.The buffer 110 can send the buffered raw and/or pre-processed signals tothe prediction block 112. In particular embodiments, the buffer 110 maygenerate (d₁, d₂, . . . , d_(N)) to send to the prediction block 112.

In particular embodiments, the prediction block 112 can generate atime-lapse (dynamic) prediction of the user action 102 from multiplesignals using the statistical time-lapse model 114. In particularembodiments, the prediction block 112 can use the buffered raw and/orpre-processed signals (d₁, d₂, . . . , d_(N)) to generate the time-lapse(dynamic) prediction of the user action 102 from multiple signals (x₁,x₂, . . . , x_(N)). The time-lapse prediction of user action 102 maycomprise a set of predicted coordinates, which are an estimate of a setof actual coordinates. In particular embodiments, an output device (notshown) can receive the predicted coordinates. In particular embodiments,the output device can be the HID. The output device can display thepredicted coordinates on the HID.

Statistical Inference to Enhance the Precision of SparsifiedCapacitive-Touch and Other Human-Interface Devices

In particular embodiments, an electronic device comprising a sparsifiedsensor structure can use statistical inference to enhance the precisionof the sparsified sensor structure. Current touch input technologies maybecome expensive when scaled to large surfaces or non-flat applications,such as large-scale non-flat (e.g., curved) TVs. Most of the touch inputtechnologies may be manufactured onto a rigid glass substrate using amulti-layered, row-column matrix using high conductive material such asITO. Therefore, the cost for large touch screens is high. In addition,there may be a lack of flexible and non-flat (or irregular shaped) touchcapability surfaces.

In particular embodiments, to address the issues associated withscalability and unconventional surface shapes, sparsified sensor designsor structures may be used to improve the cost analysis of the resultingcapacitive-touch devices. As used herein, “sparsified sensor structureor design” may refer to a sensor structure or sensor design implementedwithin an HID, such as a touchscreen, where one or more sensors arespaced apart at least a threshold distance from each other. Inparticular embodiments, the sparsified sensor structure can comprisesets of sensors that are spaced apart at least a threshold distance fromeach other. As an example and not by way of limitation, a set of sensorsof the HID may be positioned in a pattern on the HID, such as a stripepattern. Although this disclosure describes sparsified sensor structuresin a particular manner, this disclosure contemplates sparsified sensorstructures in any suitable manner.

Certain technical challenges exist for implementing sparsified sensorstructures in a HID. One technical challenge may include a degradationof accuracy of the touch location. The solution presented by theembodiments disclosed herein to address this challenge may be to use oneor more generative models that comprise one or more system parametersand one or more latent parameters, where the one or more generativemodels are used to determine context-dependent statistics to apply adelta change to a set of detected coordinates to determine a set ofpredicted coordinates. Another technical challenge may include signalhysteresis at an onset and termination of touching and similarnon-stationarity in continuous-touch scenarios when using a sparsifiedsensor structure in a HID. The solution presented by the embodimentsdisclosed herein to address this challenge may be using a buffer tostore sensor data, where the buffer is used to remove signal noiseassociated with a set of detected coordinates form the sensor data.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includetime-lapse coordinate prediction informed by physics of HID and tactiledevices. Another technical advantage of the embodiments may includetime-lapse coordinate prediction informed by learned and recognizablepatterns of user behavior. Another technical advantage of theembodiments may include a processing methodology that can be adapted toand combined with existing coordinate estimation algorithms andpipelines used in tactile and haptic HID to enhance the accuracy oftheir output. Another technical advantage of the embodiments may includea processing methodology that could infer non-stationary andspatially-variable statistics of the raw data and sensor output toproduce enhanced-precision touch detection and location for sparsifiedcapacitive and capacitive/resistive touch-screen HIDs. Certainembodiments disclosed herein may provide none, some, or all of the abovetechnical advantages. One or more other technical advantages may bereadily apparent to one skilled in the art in view of the figures,descriptions, and claims of the present disclosure.

In particular embodiments, d₁, . . . , d_(N)=(d₁ ^(i), d₂ ^(i), . . . ,d_(N) ^(i)), i=1, 2, . . . , M (1) can be a sequence of raw or processedtemporal data samples including, without limitation, raw or processedsystem parameters, such as readings of voltage and/or current taken atthe terminals of a capacitive/resistive tactile or haptichuman-interface device. Such devices include, without limitation, anyhuman-interface device that internally captures tactile, haptic, orgestural human input in the form of M-component electrical signals. Thereadings may be taken at equally or unequally spaced, known or unknown,times t₁, . . . , t_(N), and each M-dimensional electrical signal d_(j)corresponds to a 3-component data sample x_(j) that can be interpretedas the spatial location of human input: x₁, . . . , x_(N)=(x₁ ^(i), x₂^(i), . . . , x_(N) ^(i)), i=1, 2, 3. (2)

In particular embodiments, a system and method for estimating the actualcoordinates of human input (2) from raw or processed data samples (1)for a capacitive/resistive tactile, haptic or other human interfacedevice (HID) can be generally described by a statistical model of thefollowing form: d_(i)˜p(d_(i)|x_(i), x_(i−i), . . . , x_(i−n+1), Δ). (3)

In equation (3), p can be a conditional probability distribution thatdescribes the distribution of system parameters at a moment t_(i) as afunction of current and previous actual coordinates of human input. Theparameter n may describe system hysteresis, i.e., dependence of itscurrent state on its history. For example, in the context of capacitivetactile devices, the previous relative position of tactile interfaces(e.g., the air gap between the touchscreen and stylus) may impact thestrength and quality of the current raw signal d_(i). There may exist avector of latent variables A, known or unknown parameters, that, inconjunction with a set of system parameters (raw or processed signalreadings) d_(i), d_(i−1), . . . d_(i−n+1), uniquely determines thesystem's current state at moment t_(i). For multi-point tactileinterfaces, some or all of t_(i) may be the same.

In particular embodiments, the actual coordinates (x_(i), . . . ,x_(i−K+1)) at one or more consecutive moments of time (t_(i), . . . ,t_(i−K+1)) can be predicted by sampling from an estimated stochasticmodel (x_(i), . . . , x_(i−K+1))˜{tilde over (p)}(d_(i), d_(i−1), . . .d_(i−L+1),λ), (4) where {tilde over (p)} may be a probabilitydistribution for a vector of actual coordinates given a history ofsystem parameters (raw readings) d_(i), d_(i−1), . . . d_(i−L+1) and aset of latent, known or unknown system parameters {tilde over (λ)}.Parameters K and L implicitly depend on system hysteresis, and areeither selected empirically, or estimated as part of stochastic modeltraining described below. In most cases of practical interest, p and{tilde over (p)} may not be known in a closed form and require to beestimated by training a suitable statistical model of the device.

In particular embodiments, real-time statistical coordinate inferencemay be used. A generative model described by a nonlinear maximuma-posteriori map F:{tilde over (λ)}, d_(i), d_(i−1), . . .d_(i−L+1)→x_(i), . . . , x_(i−K+1)↔x_(j) ^(i)=argmax {tilde over(p)}(x_(i)|d_(i), d_(i−1), . . . d_(i−L+1),{tilde over (λ)}), (5) andparametrized by parameters {tilde over (λ)}. Likewise, F can be asampling map, in which case {tilde over (λ)} may contain both non-randomand random components. For example, the map (5) can be component-wisecomprised of, without limitation, linear, polynomial, spline, or anyother feature-space mappings ϕ_(sj) ^(i)(d_(i), d_(i−1), . . .d_(i−L+1)) with coefficients or weights making up (a subset of) thevector {tilde over (λ)}ϵ(c_(sj) ^(i)):x _(j) ^(i)=Σ_(s) c _(sj) ^(i)ϕ_(sj) ^(i)(d _(i) , d _(i−1) , . . . d_(i−L+1)),{tilde over (λ)}=(c _(sj) ^(i)), i=1, 2, 3, j=i, i−1, . . . ,i−L+1, s=1, 2, . . . , N _(b).  (6)

Next, the model (6) may be trained—i.e., identify its parameters {tildeover (λ)} using generated training samples of raw or processed readingsd_(i), d_(i−1), d_(i−L+1) and the corresponding actual coordinatesx_(i), . . . , X_(i−K+1). The parameter estimation may be conducted byadjusting the parameters to minimize a measure of misfit between thepredicted F({tilde over (λ)}, d_(i), d_(i−1), d_(i−L+1)) and observedcoordinates x_(i), . . . , X_(i−K+1). The resulting estimated parameters{tilde over (λ)} and map (5) are then implemented in a built-incomputational unit of an HID and used for real-time prediction/samplingof x_(i), . . . , X_(i−K+1) given d_(i), d_(i−1), . . . d_(i−L+1).Parameters {tilde over (λ)} may include latent variables that expressthe model's sensitivity to unmeasured mutable external factors, such asenvironmental conditions, device locations, event times, etc. such asshown in FIG. 7 .

In particular embodiments, the proposed algorithm can be implemented asan “inference processor” in a built-in controller of a device thatutilizes a human interface technology (such as, without limitation, atouchscreen). The inference may be applied to buffered raw and/orpre-processed data that form a time-lapse series. In addition toutilizing time-lapse input data, the statistical model of systemresponse and user behavior can capture the system's dependence on thecontext in which the device is being operated (e.g., environmental orbehavioral factors) via explicit and/or latent model parameters {tildeover (λ)} described in equations (4-6) above.

In particular embodiments, the system may allow potentially very complexand analytically intractable statical relations between the actualcoordinates of human input and internal device readings. The use ofpotentially very complex and analytically intractable statical relationsmay allow for using sparsified or otherwise constrained sensorarchitectures that may be more sensitive to spurious signals, prone tohysteresis, and require complex statistical model of system response.Additionally, by allowing arbitrary feature-space functions (6), thefull apparatus of kernel methods and random processes can be leveraged.

In particular embodiments, the map (5) can be described, withoutlimitation, by a neural network, with the parameter vector {tilde over(λ)} now representing its weights, biases, and both estimated and randomlatent variables. The network in question can be, without limitation, aconvolutional neural network, a deep or restricted Boltzmann Machine, ashallow or deep network. This further increases the capacity of thegenerative model (5) and leverages the well-developed apparatus of deeplearning.

While the raw data samples (1) are generally assumed to berepresentative of the device's internal parameters (e.g., voltagereadings at terminal electrodes), in particular embodiments, where thedata samples (1) are themselves outputs of an independent processingalgorithm, such as, without limitation, the neural network-basedcoordinate prediction algorithm and processing pipeline. In such a case,one or more algorithms can function as a post-processing step,implemented on top of an existing single or multiple touch predictionframework.

While the training procedure of particular embodiments do not restrictthe type and quantity of input d_(i), d_(i−1), . . . d_(i−L+1) andoutput x_(i), . . . , X_(i−K+1) training samples, additional contextualinformation, such as continuity or discontinuity of the correspondingstrokes generated by an automated data collection system, as well asadditional system characteristics such as the subsystem or part of thedevice where the data collection is performed, can provide additionalconstraints on system latent variables.

In particular embodiments, a generative model (4,5,6) can be used wherethe feature-space function are splines, and component-wise spatiallyheterogeneous coordinate variances σ_(i) ² are part of the resolvedsystem parameter vector {tilde over (λ)}=(c_(sj) ^(i), σ_(i) ²(x_(j)^(i))).

The statistical model x_(j) ^(i)˜N (Σ_(s)c_(sj) ^(i)ϕ_(sj) ^(i)(d_(i),d_(i−1), . . . d_(i−L+1)); σ_(i) ²(x_(j) ^(i))), i=1, 2, 3, j=i, i−1, .. . i−L+1, s=1, 2, . . . , N_(b). (7) may assume normally distributed(p=N) coordinate readings. However, in particular embodiments, thestatistical model (7) may naturally extend to arbitrary analytical,parametrized, or otherwise computable probability distributions p. Notethat the spatially-variable variances σ_(i) ²(x_(j) ^(i)) represent inthis case latent dependency of system response on physical location oftouch events (e.g., closer to the edges versus middle of thetouchscreen), making up the latent part of the vector {tilde over (λ)}as discussed after equation (6) and in FIG. 7 .

In particular embodiments, the electronic device (e.g., an electronicdevice coupled with the prediction system 100) may receive sensor dataindicative of a touch input from one or more sensors of a humaninterface-device (HID). As an example and not by way of limitation, auser may use one of a finger or stylus to touch the HID, such as atouchscreen display. The touch input may occur at a set of actualcoordinates with respect to the HID. The sensor data may indicate thetouch input occurs at a set of detected coordinates with respect to theHID, where the set of detected coordinates may be different from the setof actual coordinates. In particular embodiments, the electronic devicemay store the sensor data in a buffer. As an example and not by way oflimitation, the electronic device can store sensor data within a 100 msbuffer. The buffer can be used to remove signal noise associated withthe set of detected coordinates from the sensor data. The context may bedetermined based on the stored data. In particular embodiments, the oneor more sensors of the HID may be positioned in a pattern on the HID. Inparticular embodiments, the pattern may comprise a stripe pattern. Afirst subset of sensors of the one or more sensor may be positioned atleast a threshold distance away from a second subset of sensors of theone or more sensors. In particular embodiments, the first subset ofsensors and the second subset of sensors may be positioned alongparallel lines with respect to the HID. In particular embodiments, theone or more sensors can comprise one or more of a capacitive sensor or aresistive sensor. Although this disclosure describes receiving sensordata in a particular manner, this disclosure contemplates receivingsensor data in any suitable manner.

In particular embodiments, the electronic device may determine a contextassociated with the touch input. To determine the context associatedwith the touch input, the electronic device may use one or more sensorsof a HID, access a context database for information corresponding to theHID, or access information through one or more of first-party sources orthird-party sources as described herein. In particular embodiments, thecontext can comprise information corresponding to one or more of systemparameters and latent parameters. Although this disclosure describesdetermining a context in a particular manner, this disclosurecontemplates determining a context in any suitable manner.

In particular embodiments, the electronic device may determinecontext-dependent statistics to apply a delta change to the set ofdetected coordinates. The electronic device may use one or moregenerative models to determine the context-dependent statistics to applythe delta change to the set of detected coordinates. Thecontext-dependent statistics may be based on the context associated withthe touch input. The one or more generative models may comprise one ormore system parameters and one or more latent parameters. In particularembodiments, the each of the one or more generative models may be amachine-learning model. Although this disclosure describes determiningcontext-dependent statistics in a particular manner, this disclosurecontemplates determining context-dependent statistics in any suitablemanner.

In particular embodiments, the electronic device may determine a set ofpredicted coordinates of the touch input with respect to the HID. Theelectronic device may determine a set of time-lapsed predictedcoordinates of the touch input with respect to the HID based on thedelta change. As an example and not by way of limitation, the electronicdevice may adjust the set of detected coordinates by the delta change todetermine the set of time-lapsed predicted coordinates. The set oftime-lapsed predicted coordinates may be an estimate of the set ofactual coordinates. In particular embodiments, the electronic device maydisplay, on the HID, an indication of the set of time-lapsed predictedcoordinates of the touch input in real-time. As an example and not byway of limitation, as a user is touching a HID of an electronic device,the electronic device may determine the set of time-lapsed predictedcoordinates corresponding to the touch input and display the set oftime-lapsed predicted coordinates on the HID. Although this disclosuredescribes determining a set of predicted coordinates in a particularmanner, this disclosure contemplates determining a set of predictedcoordinates in any suitable manner.

In particular embodiments, the electronic device may generate the sensordata. The electronic device may inject a plurality of signals into theone or more sensors. The plurality of signals may comprise at least afirst signal at a first frequency and a second signal at a secondfrequency. As an example and not by way of limitation, the electronicdevice may inject a signal at a first frequency in one source electrodeand inject a signal at a second frequency in another source electrode.In particular embodiments, the electronic device may detect, by the oneor more sensors, a plurality of attenuated measured signals based on thetouch input interfacing the plurality of signals. A read electrode maybe placed in between the two source electrodes. As the electronic devicereceives the touch input via the HID, the plurality of signals (that areinjected by the electronic device) are attenuated. The electronic devicemay use the read electrode to detect the attenuated measured signals.The plurality of attenuated measured signals may be used to generate thesensor data. Although this disclosure describes generating sensor datain a particular manner, this disclosure contemplates generating sensordata in any suitable manner.

FIG. 2 illustrates an example display 202 with a sparsified sensorstructure. In particular embodiments, the display 202 can comprise apolyethylene terephthalate (PET) substrate 204 coated with resistivematerial, poly (3,4-ethylenedioxythiophene) polystyrene sulfonate(PEDOT:PSS) 206 in a pattern, such as stripes. In particularembodiments, the coating may be applied using a mask or laser itchingafter a uniform coating. In particular embodiments, each of theresistive materials 206 can be coupled with a pair of electrodes 208,210. Each resistive material 206 coupled with a pair of electrodes 208,210 can be a sensor. As an example and not by way of limitation, theresistive material 206 a is coupled to a first electrode 208 a and asecond electrode 210 a. As another example and not by way of limitation,the resistive material 206 b is coupled to a first electrode 208 b and asecond electrode 210 b. As another example and not by way of limitation,the resistive material 206 c is coupled to a first electrode 208 c and asecond electrode 210 c. As another example and not by way of limitation,the resistive material 206 d is coupled to a first electrode 208 d and asecond electrode 210 d. As another example and not by way of limitation,the resistive material 206 e is coupled to a first electrode 208 e and asecond electrode 210 e. As another example and not by way of limitation,the resistive material 206 f is coupled to a first electrode 208 f and asecond electrode 210 f As another example and not by way of limitation,the resistive material 206 g is coupled to a first electrode 208 g and asecond electrode 210 g. As another example and not by way of limitation,the resistive material 206 h is coupled to a first electrode 208 h and asecond electrode 210 h. In particular embodiments, the resistivematerials 206 a-206 d and the respective electrodes 208 a-208 d, 210a-210 d can be grouped into a first subset of sensors and the resistivematerials 206 e-206 h and the respective electrodes 208 e-208 h, 210e-210 h can be grouped into a second subset sensors. The two separatesubsets can be separated by at least a threshold distance. Each of theresistive materials 206 and their respective electrodes 208, 210 can beseparated from each other by a predetermined distance. While thisdisclosure shows a particular number of the different components of thedisplay 202 in a particular arrangement, this disclosure contemplatesany suitable number of different components of the display 202 in anysuitable arrangement. As an example and not by way of limitation, theremay be two additional resistive materials 206 and correspondingelectrodes in each of the two subsets. As another example and not by wayof limitation, each of the subsets can be shaped in a different pattern,such as diagonal stripes (instead of vertical stripes) along the PETsubstrate 204.

Referring to FIG. 2 , a cross-sectional view 210 of the stripe of thedisplay 202 is shown. The cross-sectional view 210 includes a view ofthe different layers 204, 206, 212, 214, 216, 220 of the stripe of thedisplay 202. The stripe of the display 202 may comprise a glass layer212, on top of an optically clear adhesive (OCA) layer 214, on top ofthe resistive material (PEDOT:PSS) 206, on top of the PET substrate 204,on top of an air gap layer 216, and on top of the liquid crystal display(LCD) 220. As an example and not by way of limitation, the glass layer212 can be 2.8 mm thick and the air gap layer 216 can be 3.2 mm thick.While this disclosure shows a specific number of different components ofthe stripe of the display 202 arranged in a particular manner, thisdisclosure contemplates any number of different components of the stripeof the display 202 arranged in any suitable manner. As an example andnot by way of limitation, the glass layer 212 can be thicker, such as3.0 mm. As another example and not by way of limitation, the LCD 220 canbe replaced with an organic light-emitting diode (OLED).

FIG. 3 illustrates an example circuit 300 of a sparsified sensorstructure. In particular embodiments, a user can touch the circuit 300using a touch input 302, which would create a path to ground 304. Thetouch input 302 can be a user's finger. In particular embodiments, thecircuit 300 may include source electrodes 306 a-306 d, signals 308 a-308d, read electrodes 310 a-310 b, resistance 312, capacitance 314,electric fields 316 a-316 b, and touch capacitance 318. The sourceelectrodes 306 a and 306 c both form a stripe, the read electrodes 310 aand 310 b form a stripe, and the source electrodes 306 b-306 d form astripe. The signal variation that is detected when the touch input 302touches the circuit 300 comes from shunting the electric field 316 a. Inparticular embodiments, there are signals 308 a-308 d injected into thesource electrodes 306 a-306 d, respectively. These signals are readusing read electrodes 310 a-310 b that are adjacent to the sourceelectrodes 306 a-306 d. The equivalent circuit of the stripe-TV forms alow pass filter that has a cutoff frequency

$f_{c} = {\frac{1}{2\pi{RC}}.}$The injected signal frequencies of the signals 308 a-308 d have beenchosen to be around the f_(c) to increase the sensitivity to the touchinput 302. When a touch input 302 touches the stripe at a distance d anew capacitance (touch capacitance) is added to the circuit 300. Themeasured signal will be attenuated. The attenuation can be proportionalto the distance between the touch input 302 and the read electrode 310.The circuit 300 can apply different signals 308 a-308 d to the stripes,which allow detection of the touch input 302 on, left, or right of thereading stripe that comprises the two read electrodes 310 a-310 b. Thesignals 308 a-308 d can each be a single or multiple frequencies.

In particular embodiments, the touch capacitance 318 can vary with touchpressure, finger area, and the like. The measured signals at the readelectrodes 310 a-310 b can be normalized to eliminate the variations incapacitive effects. As shown in FIG. 3 , since the touch input 302 islocated in a touch location between the sourcing stripe (that comprisesthe two source electrodes 306 a-306 c) and the reading stripe, themeasured signal (e.g., signal 306 a) will be attenuated more than theother signals 306 b-306 d. As a result of the attenuated measured signal(e.g., signal 306 a), a set of detected coordinates of the touch input302 can be determined in between the reading stripe and the sourcingstripe. In particular embodiments, the signal frequencies of the signals308 a-308 d may be chosen to be into two different bands (low and high).The low frequencies can travel farther while the high frequencies aremore sensitive to the touch input 302. In particular embodiments, thecapacitance 318 can have a higher capacitance than the other capacitance314 in the circuit 300.

FIG. 4 illustrates an example power spectral density (PSD) chart of asparsified sensor structure. In particular embodiments, each of the foursignals may have two frequencies from the adjacent stripes. The readingstripe may have a modulated signal that contains eight differentfrequencies. The PSD chart plots the power of a signal as a function offrequency. The solid lines correspond to the top stripe and the dottedlines correspond to the bottom stripe.

FIG. 5A illustrates an example touch coordinate prediction from touchinputs. In particular embodiments, a set of detected coordinates can bedisplayed on a HID as shown in FIG. 5A as a result of a user using atouch input on the HID. The numbers illustrated in FIG. 5A can indicatea sequence of touch inputs that the HID received. For example, the firsttouch input started at “0” where a line is drawn. Referring to FIG. 5B,a chart is shown where a set of detected X-coordinates and detectedY-coordinates are plotted along a time index. The numbers correspondingto the sequence of touch inputs that the HID received are shown alongthe time index to indicate which touch input the respective X segmentsand Y segments correspond to. As shown from the chart, there may be astrong vertical variance and hysteresis. The hysteresis is morenoticeable near the terminal events of each segment (X and Y segments).FIGS. 5A-5B may illustrate an issue that comes from using an HID with asparsified sensor structure. That is, the accuracy of the touch locationmay be degraded.

FIG. 6 illustrates an example human-interface device (HID) 600. The HID600 can include interface objects 602, an input medium 604, HID sensors606, raw sensor data collector/processor, PLCs 610, sensors 612,oscillators 614, RF generators 616, linking hardware and wiring 618,buffered data pipeline 620, human audiovisual and tactile perception622, output medium 624, output devices 626, time-lapse data inference628, ADC 630, SBC/PLC 632, NVM 634, serial interface 636, networkinterface 638, linking hardware and wiring 640, and premises (W)LAN/WWW642. In particular embodiments, the interfacing objects 602 may includeone or more of a finger, stylus for a touchscreen, andRF/optical/acoustic source for haptic HID. The input medium 604 mayinclude a dielectric screen coating, airgaps. The output medium 624 mayinclude RF/audio waves, tactile/braille display surfaces. The outputdevices 626 may include audio-visual, tactile displays. The NVM 634 mayinclude EEPROM or Flash. The serial interface 636 may include UART or R5xxx. The network interface 638 may include IEEE 802.x.

In particular embodiments, the HID 600 can receive a touch input from aninterfacing object 602 through input medium 604 with the HID sensors606. The raw sensor data collector/processor 608 can process theincoming touch input and send it to the buffered data pipeline 620. Thebuffered data pipeline 620 can send the buffered data to the time-lapsedata inference processor 628 which may process the buffered data todetermine a set of predicted coordinates based on the received touchinput. The time-lapse data inference processor 628 can send the set ofpredicted coordinates to the output devices 626 to present to a user.

FIG. 7 illustrates an example process flow 700 of a HID to determine aset of predicted coordinates. The process 700 may begin with receiving auser action 702 and other factors 704. The user action 702 can beembodied as a touch input, such as finger/stylus motion and location.The other factors 704 can be embodied as time, environmental factors,device/event location, usage context, operator profile, and the like.The user action 702 and other factors 704 are collected into the systemchanges 706. The other factors 704 can also be sent to a contextualizedtime-lapse statistical model. The system changes 706 are then processedto read raw sensory data 708. The raw sensory data 708 can be sent to abuffer 712 and go through pre-processing 710, where the raw sensory data708 and the user action is tentatively interpreted using anon-time-lapse model. The pre-processing 710 can determine a set ofdetected coordinates corresponding to the user action 702. The set ofdetected coordinates may be sent to the buffer 712. The process 700 maythen buffer both the raw sensory data 708 and the output of thepre-processing 710 (e.g., initially processed data) into a time-lapseseries. The output of the buffer 712 can be sent to the contextualizedtime-lapse statistical model 714, where the model interprets the currentuser action (e.g., determine a set of actual coordinates for a touchinput) and update earlier interpretations of preceding user actions byusing the contextualized time-lapse statistical model 714 of systemresponse and user behavior. The output of the contextualized time-lapsestatistical model 714 can be an interpreted user action(s). As anexample and not by way of limitation, the output of the contextualizedtime-lapse statistical model 714 can be a set of predicted coordinates.The process 700 can then feed the interpreted user action(s) intorelevant applications 716. As an example and not by way of limitation,the process 700 can then send the interpreted user action(s) to a cameraapplication to add marking to a captured image.

FIGS. 8A-8B illustrate an example process of estimating a set of actualcoordinates associated with a touch input. FIG. 8A shows a HIDpresenting a set of detected coordinates. After processing using themethods and processes described herein, FIG. 8B shows a post-processedoutput including a set of predicted coordinates. As shown in FIG. 8B,the post-processed output is smoother than the original set of detectedcoordinates shown in FIG. 8A. In particular embodiments, the HID canperform an inference of the set of actual coordinates from the set ofdetected coordinates by regression using spline feature-space mappingsin a model.

FIGS. 9A-9B illustrate an example post-processing applied to touchinputs. FIG. 9A shows a HID presenting a set of detected coordinates.After processing using the methods and processes described herein, FIG.9B shows a post-processed output including a set of predictedcoordinates. As shown in FIG. 9B, the post-processed output corrects theedge issues of the original set of detected coordinates shown in FIG.9A. In particular embodiments, the HID can perform an inference of theset of actual coordinates from the set of detected coordinates byallowing for a spatially-variable coordinate variance in a statisticalmodel.

FIG. 10 illustrates an example training process 1000 of estimating a setof actual coordinates associated with a touch input. The process 1000can start with manually and automatically generating synthetictime-lapse data 1002. By generating synthetic time-lapse data 1002, theprocess 1000 can extract both raw data readings 1004 and actualcoordinates 1006, which may be sent to be processed by a generativemodel (5). The generative model (5) may be implemented 1010 into adevice's built-in controller 1012, and applied in real time to touchlocation and prediction. In particular embodiments, the controller 1012can perform a process 1016 of estimating actual coordinates of a touchinput. When raw data readings 1028 are received from an HID, the rawdata readings 1028 are sent to controller 1012 to process using thegenerative model 1010. The controller 1012 can output a set of predictedcoordinates 1014, which are outputted to the HID through the process1016.

FIG. 11 illustrates is a flow diagram of a method 1100 for determining aset of predicted coordinates of a touch input, in accordance with thepresently disclosed embodiments. The method 1100 may be performedutilizing one or more processing devices (e.g., electronic devicecoupled with a prediction system 100) that may include hardware (e.g., ageneral purpose processor, a graphic processing unit (GPU), anapplication-specific integrated circuit (ASIC), a system-on-chip (SoC),a microcontroller, a field-programmable gate array (FPGA), a centralprocessing unit (CPU), an application processor (AP), a visualprocessing unit (VPU), a neural processing unit (NPU), a neural decisionprocessor (NDP), or any other processing device(s) that may be suitablefor processing 2D and 3D image data, software (e.g., instructionsrunning/executing on one or more processors), firmware (e.g.,microcode), or some combination thereof.

The method 1100 may begin at step 1110 with the one or more processingdevices (e.g., electronic device coupled with prediction system 100)receiving sensor data indicative of a touch input from one or moresensors of a human interface-device (HID) of the electronic device. Forexample, in particular embodiments, the touch input may occur at a setof actual coordinates with respect to the HID. The sensor data mayindicate the touch input occurs at a set of detected coordinates withrespect to the HID. The set of detected coordinates may be differentfrom the set of actual coordinates. The method 1100 may then continue atstep 1120 with the one or more processing devices (e.g., electronicdevice coupled with prediction system 100) determining a contextassociated with the touch input. The method 1100 may then continue atstep 1130 with the one or more processing devices (e.g., electronicdevice coupled with prediction system 100) determining, by one or moregenerative models, context-dependent statistics to apply a delta changeto the set of detected coordinates. For example, in particularembodiments, the context-dependent statistics may be based on thecontext associated with the touch input and the one or more generativemodels may comprise one or more system parameters and one or more latentparameters. The method 1100 may then continue at block 1140 with the oneor more processing devices (e.g., electronic device coupled withprediction system 100) determining a set of time-lapsed predictedcoordinates of the touch input with respect to the HID based on thedelta change. For example, in particular embodiments, the set oftime-lapsed predicted coordinates are an estimate of the set of actualcoordinates. Particular embodiments may repeat one or more steps of themethod of FIG. 11 , where appropriate. Although this disclosuredescribes and illustrates particular steps of the method of FIG. 11 asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method of FIG. 11 occurring in any suitable order.Moreover, although this disclosure describes and illustrates an examplemethod for determining a set of predicted coordinates of a touch inputincluding the particular steps of the method of FIG. 11 , thisdisclosure contemplates any suitable method for determining a set ofpredicted coordinates of a touch input including any suitable steps,which may include all, some, or none of the steps of the method of FIG.11 , where appropriate. Furthermore, although this disclosure describesand illustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 11 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 11 .

Systems and Methods

FIG. 12 illustrates an example computer system 1200 that may be utilizedto perform determining a set of predicted coordinates of a touch input,in accordance with the presently disclosed embodiments. In particularembodiments, one or more computer systems 1200 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1200 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1200 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1200.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1200. This disclosure contemplates computer system 1200 taking anysuitable physical form. As example and not by way of limitation,computer system 1200 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (e.g., acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, anaugmented/virtual reality device, or a combination of two or more ofthese. Where appropriate, computer system 1200 may include one or morecomputer systems 1200; be unitary or distributed; span multiplelocations; span multiple machines; span multiple data centers; or residein a cloud, which may include one or more cloud components in one ormore networks.

Where appropriate, one or more computer systems 1200 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example, and not byway of limitation, one or more computer systems 1200 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1200 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1200 includes a processor1202, memory 1204, storage 1206, an input/output (I/O) interface 1208, acommunication interface 1210, and a bus 1212. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.In particular embodiments, processor 1202 includes hardware forexecuting instructions, such as those making up a computer program. Asan example, and not by way of limitation, to execute instructions,processor 1202 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1204, or storage 1206; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1204, or storage 1206. In particularembodiments, processor 1202 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1202 including any suitable number of any suitable internal caches,where appropriate. As an example, and not by way of limitation,processor 1202 may include one or more instruction caches, one or moredata caches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1204 or storage 1206, and the instruction caches may speed upretrieval of those instructions by processor 1202.

Data in the data caches may be copies of data in memory 1204 or storage1206 for instructions executing at processor 1202 to operate on; theresults of previous instructions executed at processor 1202 for accessby subsequent instructions executing at processor 1202 or for writing tomemory 1204 or storage 1206; or other suitable data. The data caches mayspeed up read or write operations by processor 1202. The TLBs may speedup virtual-address translation for processor 1202. In particularembodiments, processor 1202 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1202 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1202 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1202. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1204 includes main memory for storinginstructions for processor 1202 to execute or data for processor 1202 tooperate on. As an example, and not by way of limitation, computer system1200 may load instructions from storage 1206 or another source (such as,for example, another computer system 1200) to memory 1204. Processor1202 may then load the instructions from memory 1204 to an internalregister or internal cache. To execute the instructions, processor 1202may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1202 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1202 may then write one or more of those results to memory 1204. Inparticular embodiments, processor 1202 executes only instructions in oneor more internal registers or internal caches or in memory 1204 (asopposed to storage 1206 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1204 (asopposed to storage 1206 or elsewhere).

One or more memory buses (which may each include an address bus and adata bus) may couple processor 1202 to memory 1204. Bus 1212 may includeone or more memory buses, as described below. In particular embodiments,one or more memory management units (MMUs) reside between processor 1202and memory 1204 and facilitate accesses to memory 1204 requested byprocessor 1202. In particular embodiments, memory 1204 includes randomaccess memory (RAM). This RAM may be volatile memory, where appropriate.Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM(SRAM). Moreover, where appropriate, this RAM may be single-ported ormulti-ported RAM. This disclosure contemplates any suitable RAM. Memory1204 may include one or more memory devices 1204, where appropriate.Although this disclosure describes and illustrates particular memory,this disclosure contemplates any suitable memory.

In particular embodiments, storage 1206 includes mass storage for dataor instructions. As an example, and not by way of limitation, storage1206 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1206 may include removable or non-removable (or fixed)media, where appropriate. Storage 1206 may be internal or external tocomputer system 1200, where appropriate. In particular embodiments,storage 1206 is non-volatile, solid-state memory. In particularembodiments, storage 1206 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1206taking any suitable physical form. Storage 1206 may include one or morestorage control units facilitating communication between processor 1202and storage 1206, where appropriate. Where appropriate, storage 1206 mayinclude one or more storages 1206. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1208 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1200 and one or more I/O devices. Computersystem 1200 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1200. As an example, and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1206 for them. Where appropriate, I/Ointerface 1208 may include one or more device or software driversenabling processor 1202 to drive one or more of these I/O devices. I/Ointerface 1208 may include one or more I/O interfaces 1206, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1210 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1200 and one or more other computer systems 1200 or oneor more networks. As an example, and not by way of limitation,communication interface 1210 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1210 for it.

As an example, and not by way of limitation, computer system 1200 maycommunicate with an ad hoc network, a personal area network (PAN), alocal area network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), or one or more portions of the Internet or a combinationof two or more of these. One or more portions of one or more of thesenetworks may be wired or wireless. As an example, computer system 1200may communicate with a wireless PAN (WPAN) (such as, for example, aBLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephonenetwork (such as, for example, a Global System for Mobile Communications(GSM) network), or other suitable wireless network or a combination oftwo or more of these. Computer system 1200 may include any suitablecommunication interface 1210 for any of these networks, whereappropriate. Communication interface 1210 may include one or morecommunication interfaces 1210, where appropriate. Although thisdisclosure describes and illustrates a particular communicationinterface, this disclosure contemplates any suitable communicationinterface.

In particular embodiments, bus 1212 includes hardware, software, or bothcoupling components of computer system 1200 to each other. As anexample, and not by way of limitation, bus 1212 may include anAccelerated Graphics Port (AGP) or other graphics bus, an EnhancedIndustry Standard Architecture (EISA) bus, a front-side bus (FSB), aHYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture(ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, amemory bus, a Micro Channel Architecture (MCA) bus, a PeripheralComponent Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serialadvanced technology attachment (SATA) bus, a Video Electronics StandardsAssociation local (VLB) bus, or another suitable bus or a combination oftwo or more of these. Bus 1212 may include one or more buses 1212, whereappropriate. Although this disclosure describes and illustrates aparticular bus, this disclosure contemplates any suitable bus orinterconnect.

AI Architecture

FIG. 13 illustrates a diagram 1300 of an example artificial intelligence(AI) architecture 1302 that may be utilized to perform determining a setof predicted coordinates of a touch input, in accordance with thepresently disclosed embodiments. In particular embodiments, the AIarchitecture 1302 may be implemented utilizing, for example, one or moreprocessing devices that may include hardware (e.g., a general purposeprocessor, a graphic processing unit (GPU), an application-specificintegrated circuit (ASIC), a system-on-chip (SoC), a microcontroller, afield-programmable gate array (FPGA), a central processing unit (CPU),an application processor (AP), a visual processing unit (VPU), a neuralprocessing unit (NPU), a neural decision processor (NDP), and/or otherprocessing device(s) that may be suitable for processing various dataand making one or more decisions based thereon), software (e.g.,instructions running/executing on one or more processing devices),firmware (e.g., microcode), or some combination thereof.

In particular embodiments, as depicted by FIG. 13 , the AI architecture1302 may include machine leaning (ML) algorithms and functions 1304,natural language processing (NLP) algorithms and functions 1306, expertsystems 1308, computer-based vision algorithms and functions 1310,speech recognition algorithms and functions 1312, planning algorithmsand functions 1314, and robotics algorithms and functions 1316. Inparticular embodiments, the ML algorithms and functions 1304 may includeany statistics-based algorithms that may be suitable for findingpatterns across large amounts of data (e.g., “Big Data” such as userclick data or other user interactions, text data, image data, videodata, audio data, speech data, numbers data, and so forth). For example,in particular embodiments, the ML algorithms and functions 1304 mayinclude deep learning algorithms 1318, supervised learning algorithms1320, and unsupervised learning algorithms 1322.

In particular embodiments, the deep learning algorithms 1318 may includeany artificial neural networks (ANNs) that may be utilized to learn deeplevels of representations and abstractions from large amounts of data.For example, the deep learning algorithms 1318 may include ANNs, such asa multilayer perceptron (MLP), an autoencoder (AE), a convolution neuralnetwork (CNN), a recurrent neural network (RNN), long short term memory(LSTM), a grated recurrent unit (GRU), a restricted Boltzmann Machine(RBM), a deep belief network (DBN), a bidirectional recurrent deepneural network (BRDNN), a generative adversarial network (GAN), and deepQ-networks, a neural autoregressive distribution estimation (NADE), anadversarial network (AN), attentional models (AM), deep reinforcementlearning, and so forth.

In particular embodiments, the supervised learning algorithms 1320 mayinclude any algorithms that may be utilized to apply, for example, whathas been learned in the past to new data using labeled examples forpredicting future events. For example, starting from the analysis of aknown training dataset, the supervised learning algorithms 1320 mayproduce an inferred function to make predictions about the outputvalues. The supervised learning algorithms 1320 can also compare itsoutput with the correct and intended output and find errors in order tomodify the supervised learning algorithms 1320 accordingly. On the otherhand, the unsupervised learning algorithms 1322 may include anyalgorithms that may applied, for example, when the data used to trainthe unsupervised learning algorithms 1322 are neither classified orlabeled. For example, the unsupervised learning algorithms 1322 maystudy and analyze how systems may infer a function to describe a hiddenstructure from unlabeled data.

In particular embodiments, the NLP algorithms and functions 1306 mayinclude any algorithms or functions that may be suitable forautomatically manipulating natural language, such as speech and/or text.For example, in particular embodiments, the NLP algorithms and functions1306 may include content extraction algorithms or functions 1324,classification algorithms or functions 1326, machine translationalgorithms or functions 1328, question answering (QA) algorithms orfunctions 1330, and text generation algorithms or functions 1332. Inparticular embodiments, the content extraction algorithms or functions1324 may include a means for extracting text or images from electronicdocuments (e.g., webpages, text editor documents, and so forth) to beutilized, for example, in other applications.

In particular embodiments, the classification algorithms or functions1326 may include any algorithms that may utilize a supervised learningmodel (e.g., logistic regression, naïve Bayes, stochastic gradientdescent (SGD), k-nearest neighbors, decision trees, random forests,support vector machine (SVM), and so forth) to learn from the data inputto the supervised learning model and to make new observations orclassifications based thereon. The machine translation algorithms orfunctions 1328 may include any algorithms or functions that may besuitable for automatically converting source text in one language, forexample, into text in another language. The QA algorithms or functions1330 may include any algorithms or functions that may be suitable forautomatically answering questions posed by humans in, for example, anatural language, such as that performed by voice-controlled personalassistant devices. The text generation algorithms or functions 1332 mayinclude any algorithms or functions that may be suitable forautomatically generating natural language texts.

In particular embodiments, the expert systems 1308 may include anyalgorithms or functions that may be suitable for simulating the judgmentand behavior of a human or an organization that has expert knowledge andexperience in a particular field (e.g., stock trading, medicine, sportsstatistics, and so forth). The computer-based vision algorithms andfunctions 1310 may include any algorithms or functions that may besuitable for automatically extracting information from images (e.g.,photo images, video images). For example, the computer-based visionalgorithms and functions 1310 may include image recognition algorithms1334 and machine vision algorithms 1336. The image recognitionalgorithms 1334 may include any algorithms that may be suitable forautomatically identifying and/or classifying objects, places, people,and so forth that may be included in, for example, one or more imageframes or other displayed data. The machine vision algorithms 1336 mayinclude any algorithms that may be suitable for allowing computers to“see”, or, for example, to rely on image sensors cameras withspecialized optics to acquire images for processing, analyzing, and/ormeasuring various data characteristics for decision making purposes.

In particular embodiments, the speech recognition algorithms andfunctions 1312 may include any algorithms or functions that may besuitable for recognizing and translating spoken language into text, suchas through automatic speech recognition (ASR), computer speechrecognition, speech-to-text (STT), or text-to-speech (TTS) in order forthe computing to communicate via speech with one or more users, forexample. In particular embodiments, the planning algorithms andfunctions 1338 may include any algorithms or functions that may besuitable for generating a sequence of actions, in which each action mayinclude its own set of preconditions to be satisfied before performingthe action. Examples of AI planning may include classical planning,reduction to other problems, temporal planning, probabilistic planning,preference-based planning, conditional planning, and so forth. Lastly,the robotics algorithms and functions 1340 may include any algorithms,functions, or systems that may enable one or more devices to replicatehuman behavior through, for example, motions, gestures, performancetasks, decision-making, emotions, and so forth.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

Herein, “automatically” and its derivatives means “without humanintervention,” unless expressly indicated otherwise or indicatedotherwise by context.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Embodiments according to theinvention are in particular disclosed in the attached claims directed toa method, a storage medium, a system and a computer program product,wherein any feature mentioned in one claim category, e.g. method, can beclaimed in another claim category, e.g. system, as well. Thedependencies or references back in the attached claims are chosen forformal reasons only. However, any subject matter resulting from adeliberate reference back to any previous claims (in particular multipledependencies) can be claimed as well, so that any combination of claimsand the features thereof are disclosed and can be claimed regardless ofthe dependencies chosen in the attached claims. The subject-matter whichcan be claimed comprises not only the combinations of features as setout in the attached claims but also any other combination of features inthe claims, wherein each feature mentioned in the claims can be combinedwith any other feature or combination of other features in the claims.Furthermore, any of the embodiments and features described or depictedherein can be claimed in a separate claim and/or in any combination withany embodiment or feature described or depicted herein or with any ofthe features of the attached claims.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by an electronic device:receiving sensor data indicative of a touch input from one or moresensors of a human interface-device (HID) of the electronic device,wherein the touch input occurs at a set of actual coordinates withrespect to the HID, and wherein the sensor data indicates the touchinput occurs at a set of detected coordinates with respect to the HID,wherein the set of detected coordinates is different from the set ofactual coordinates, wherein the sensor data indicative of a touch input,the set of actual coordinates, and the set of detected coordinates eachcomprise a sequence of locations on the HID, each location in thesequence corresponding to a particular time; determining a contextassociated with the touch input; determining, by one or more generativemodels, context-dependent statistics to apply a delta change to the setof detected coordinates, wherein the context-dependent statistics arebased on the context associated with the touch input, and wherein theone or more generative models comprises one or more system parametersand one or more latent parameters, and wherein the delta changecorresponds to at least one first location in the sequence of locationsof the set of detected coordinates, and the delta change correspondingto the at least one first location is determined based at least on oneor more other locations in the sequence of locations of the set ofdetected coordinates; and determining a set of time-lapsed predictedcoordinates of the touch input with respect to the HID based on thedelta change, wherein the set of time-lapsed predicted coordinates arean estimate of the set of actual coordinates.
 2. The method of claim 1,further comprising: storing the sensor data in a buffer, wherein thebuffer is used to remove signal noise associated with the set ofdetected coordinates from the sensor data, and wherein the context isdetermined based on the stored data.
 3. The method of claim 1, whereinthe one or more sensors of the HID are positioned in a pattern on theHID where a first subset of sensors of the one or more sensors arepositioned at least a threshold distance away from a second subset ofsensors of the one or more sensors.
 4. The method of claim 3, whereinthe pattern comprises a stripe pattern, where the first subset ofsensors and the second subset of sensors are positioned along parallellines with respect to the HID.
 5. The method of claim 1, furthercomprising: injecting a plurality of signals into the one or moresensors, wherein the plurality of signals comprise at least a firstsignal at a first frequency and a second signal at a second frequency;and detecting, by the one or more sensors, a plurality of attenuatedmeasured signals based on the touch input interfacing the plurality ofsignals, wherein the plurality of attenuated measured signals are usedto generate the sensor data.
 6. The method of claim 1, wherein the oneor more sensors comprises one or more of a capacitive sensor or aresistive sensor.
 7. The method of claim 1, further comprising:displaying, on the HID, an indication of the set of predictedcoordinates of the touch input in real-time.
 8. An electronic devicecomprising: one or more displays; one or more non-transitorycomputer-readable storage media including instructions; and one or moreprocessors coupled to the storage media, the one or more processorsconfigured to execute the instructions to: receive sensor dataindicative of a touch input from one or more sensors of a humaninterface-device (HID) of the electronic device, wherein the touch inputoccurs at a set of actual coordinates with respect to the HID, andwherein the sensor data indicates the touch input occurs at a set ofdetected coordinates with respect to the HID, wherein the set ofdetected coordinates is different from the set of actual coordinates,wherein the sensor data indicative of a touch input, the set of actualcoordinates, and the set of detected coordinates each comprise asequence of locations on the HID, each location in the sequencecorresponding to a particular time; determine a context associated withthe touch input; determine, by one or more generative models,context-dependent statistics to apply a delta change to the set ofdetected coordinates, wherein the context-dependent statistics are basedon the context associated with the touch input, and wherein the one ormore generative models comprises one or more system parameters and oneor more latent parameters, and wherein the delta change corresponds toat least one first location in the sequence of locations of the set ofdetected coordinates, and the delta change corresponding to the at leastone first location is determined based at least on one or more otherlocations in the sequence of locations of the set of detectedcoordinates; and determine a set of time-lapsed predicted coordinates ofthe touch input with respect to the HID based on the delta change,wherein the set of time-lapsed predicted coordinates are an estimate ofthe set of actual coordinates.
 9. The electronic device of claim 8,wherein the processors are further configured to execute theinstructions to: store the sensor data in a buffer, wherein the bufferis used to remove signal noise associated with the set of detectedcoordinates from the sensor data, and wherein the context is determinedbased on the stored data.
 10. The electronic device of claim 8, whereinthe one or more sensors of the HID are positioned in a pattern on theHID where a first subset of sensors of the one or more sensors arepositioned at least a threshold distance away from a second subset ofsensors of the one or more sensors.
 11. The electronic device of claim10, wherein the pattern comprises a stripe pattern, where the firstsubset of sensors and the second subset of sensors are positioned alongparallel lines with respect to the HID.
 12. The electronic device ofclaim 8, wherein the processors are further configured to execute theinstructions to: inject a plurality of signals into the one or moresensors, wherein the plurality of signals comprise at least a firstsignal at a first frequency and a second signal at a second frequency;and detect, by the one or more sensors, a plurality of attenuatedmeasured signals based on the touch input interfacing the plurality ofsignals, wherein the plurality of attenuated measured signals are usedto generate the sensor data.
 13. The electronic device of claim 8,wherein the one or more sensors comprises one or more of a capacitivesensor or a resistive sensor.
 14. The electronic device of claim 8,wherein the processors are further configured to execute theinstructions to: display, on the HID, an indication of the set ofpredicted coordinates of the touch input in real-time.
 15. Acomputer-readable non-transitory storage media comprising instructionsexecutable by a processor to: receive sensor data indicative of a touchinput from one or more sensors of a human interface-device (HID) of theelectronic device, wherein the touch input occurs at a set of actualcoordinates with respect to the HID, and wherein the sensor dataindicates the touch input occurs at a set of detected coordinates withrespect to the HID, wherein the set of detected coordinates is differentfrom the set of actual coordinates, wherein the sensor data indicativeof a touch input, the set of actual coordinates, and the set of detectedcoordinates each comprise a sequence of locations on the HID, eachlocation in the sequence corresponding to a particular time; determine acontext associated with the touch input; determine, by one or moregenerative models, context-dependent statistics to apply a delta changeto the set of detected coordinates, wherein the context-dependentstatistics are based on the context associated with the touch input, andwherein the one or more generative models comprises one or more systemparameters and one or more latent parameters, and wherein the deltachange corresponds to at least one first location in the sequence oflocations of the set of detected coordinates, and the delta changecorresponding to the at least one first location is determined based atleast on one or more other locations in the sequence of locations of theset of detected coordinates; and determine a set of time-lapsedpredicted coordinates of the touch input with respect to the HID basedon the delta change, wherein the set of time-lapsed predictedcoordinates are an estimate of the set of actual coordinates.
 16. Themedia of claim 15, wherein the instructions are further executable bythe processor to: store the sensor data in a buffer, wherein the bufferis used to remove signal noise associated with the set of detectedcoordinates from the sensor data, and wherein the context is determinedbased on the stored data.
 17. The media of claim 15, wherein the one ormore sensors of the HID are positioned in a pattern on the HID where afirst subset of sensors of the one or more sensors are positioned atleast a threshold distance away from a second subset of sensors of theone or more sensors.
 18. The media of claim 17, wherein the patterncomprises a stripe pattern, where the first subset of sensors and thesecond subset of sensors are positioned along parallel lines withrespect to the HID.
 19. The media of claim 15, wherein the instructionsare further executable by the processor to: inject a plurality ofsignals into the one or more sensors, wherein the plurality of signalscomprise at least a first signal at a first frequency and a secondsignal at a second frequency; and detect, by the one or more sensors, aplurality of attenuated measured signals based on the touch inputinterfacing the plurality of signals, wherein the plurality ofattenuated measured signals are used to generate the sensor data. 20.The media of claim 15, wherein the one or more sensors comprises one ormore of a capacitive sensor or a resistive sensor.